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name flow-nexus-swarm description Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform category orchestration tags ["swarm","workflow","cloud","agents","automation","message-queue"] version 1.0.0 requires ["flow-nexus MCP server","Active Flow Nexus account (register at flow-nexus.ruv.io)"] hooks {"pre":"echo \"🧠 Flow Nexus Swarm activated\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js pre-edit \"$FILE\" 2>/dev/null || true\nfi\n","post":"echo \"✅ Flow Nexus Swarm complete\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js post-edit \"$FILE\" \"true\" 2>/dev/null || true\nfi\n"}
Flow Nexus Swarm & Workflow Orchestration
Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.
Self-Learning Intelligence
Integrates with RuVector's Q-learning and vector memory for improved performance.
CLI: node .claude/intelligence/cli.js stats
📋 Table of Contents
Overview Swarm Management Workflow Automation Agent Orchestration Templates & Patterns Advanced Features Best Practices
Overview
Flow Nexus provides cloud-based orchestration for AI agent swarms with:
Multi-topology Support : Hierarchical, mesh, ring, and star architecturesEvent-driven Workflows : Message queue processing with async executionTemplate Library : Pre-built swarm configurations for common use casesIntelligent Agent Assignment : Vector similarity matching for optimal agent selectionReal-time Monitoring : Comprehensive metrics and audit trailsScalable Infrastructure : Cloud-based execution with auto-scaling
Swarm Management
Initialize Swarm
Create a new swarm with specified topology and configuration:
mcp__flow-nexus__swarm_init ({
topology : "hierarchical" ,
maxAgents : 8 ,
strategy : "balanced"
})
Topology Guide:
Hierarchical : Tree structure with coordinator nodes (best for complex projects)Mesh : Peer-to-peer collaboration (best for research and analysis)Ring : Circular coordination (best for sequential workflows)Star : Centralized hub (best for simple delegation)
Balanced : Equal distribution of workload across agentsSpecialized : Agents focus on specific expertise areasAdaptive : Dynamic adjustment based on task complexity
Spawn Agents Add specialized agents to the swarm:
mcp__flow-nexus__agent_spawn ({
type : "researcher" ,
name : "Lead Researcher" ,
capabilities : ["web_search" , "analysis" , "summarization" ]
})
Researcher : Information gathering, web search, analysisCoder : Code generation, refactoring, implementationAnalyst : Data analysis, pattern recognition, insightsOptimizer : Performance tuning, resource optimizationCoordinator : Task delegation, progress tracking, integration
Orchestrate Tasks Distribute tasks across the swarm:
mcp__flow-nexus__task_orchestrate ({
task : "Build a REST API with authentication and database integration" ,
strategy : "parallel" ,
maxAgents : 5 ,
priority : "high"
})
Parallel : Maximum concurrency for independent subtasksSequential : Step-by-step execution with dependenciesAdaptive : AI-powered strategy selection based on task analysis
Monitor & Scale Swarms
mcp__flow-nexus__swarm_status ({
swarm_id : "optional-id"
})
mcp__flow-nexus__swarm_list ({
status : "active"
})
mcp__flow-nexus__swarm_scale ({
target_agents : 10 ,
swarm_id : "optional-id"
})
mcp__flow-nexus__swarm_destroy ({
swarm_id : "optional-id"
})
Workflow Automation
Create Workflow Define event-driven workflows with message queue processing:
mcp__flow-nexus__workflow_create ({
name : "CI/CD Pipeline" ,
description : "Automated testing, building, and deployment" ,
steps : [
{
id : "test" ,
action : "run_tests" ,
agent : "tester" ,
parallel : true
},
{
id : "build" ,
action : "build_app" ,
agent : "builder" ,
depends_on : ["test" ]
},
{
id : "deploy" ,
action : "deploy_prod" ,
agent : "deployer" ,
depends_on : ["build" ]
}
],
triggers : ["push_to_main" , "manual_trigger" ],
metadata : {
priority : 10 ,
retry_policy : "exponential_backoff"
}
})
Dependency Management : Define step dependencies with depends_onParallel Execution : Set parallel: true for concurrent stepsEvent Triggers : GitHub events, schedules, manual triggersRetry Policies : Automatic retry on transient failuresPriority Queuing : High-priority workflows execute first
Execute Workflow Run workflows synchronously or asynchronously:
mcp__flow-nexus__workflow_execute ({
workflow_id : "workflow_id" ,
input_data : {
branch : "main" ,
commit : "abc123" ,
environment : "production"
},
async : true
})
Sync (async: false) : Immediate execution, wait for completionAsync (async: true) : Message queue processing, non-blocking
Monitor Workflows
mcp__flow-nexus__workflow_status ({
workflow_id : "id" ,
execution_id : "specific-run-id" ,
include_metrics : true
})
mcp__flow-nexus__workflow_list ({
status : "running" ,
limit : 10 ,
offset : 0
})
mcp__flow-nexus__workflow_audit_trail ({
workflow_id : "id" ,
limit : 50 ,
start_time : "2025-01-01T00:00:00Z"
})
Agent Assignment Intelligently assign agents to workflow tasks:
mcp__flow-nexus__workflow_agent_assign ({
task_id : "task_id" ,
agent_type : "coder" ,
use_vector_similarity : true
})
Vector Similarity Matching:
Analyzes task requirements and agent capabilities
Finds optimal agent based on past performance
Considers workload and availability
Queue Management Monitor and manage message queues:
mcp__flow-nexus__workflow_queue_status ({
queue_name : "optional-specific-queue" ,
include_messages : true
})
Agent Orchestration
Full-Stack Development Pattern
mcp__flow-nexus__swarm_init ({
topology : "hierarchical" ,
maxAgents : 8 ,
strategy : "specialized"
})
mcp__flow-nexus__agent_spawn ({ type : "coordinator" , name : "Project Manager" })
mcp__flow-nexus__agent_spawn ({ type : "coder" , name : "Backend Developer" })
mcp__flow-nexus__agent_spawn ({ type : "coder" , name : "Frontend Developer" })
mcp__flow-nexus__agent_spawn ({ type : "coder" , name : "Database Architect" })
mcp__flow-nexus__agent_spawn ({ type : "analyst" , name : "QA Engineer" })
mcp__flow-nexus__workflow_create ({
name : "Full-Stack Development" ,
steps : [
{ id : "requirements" , action : "analyze_requirements" , agent : "coordinator" },
{ id : "db_design" , action : "design_schema" , agent : "Database Architect" },
{ id : "backend" , action : "build_api" , agent : "Backend Developer" , depends_on : ["db_design" ] },
{ id : "frontend" , action : "build_ui" , agent : "Frontend Developer" , depends_on : ["requirements" ] },
{ id : "integration" , action : "integrate" , agent : "Backend Developer" , depends_on : ["backend" , "frontend" ] },
{ id : "testing" , action : "qa_testing" , agent : "QA Engineer" , depends_on : ["integration" ] }
]
})
mcp__flow-nexus__workflow_execute ({
workflow_id : "workflow_id" ,
input_data : {
project : "E-commerce Platform" ,
tech_stack : ["Node.js" , "React" , "PostgreSQL" ]
}
})
Research & Analysis Pattern
mcp__flow-nexus__swarm_init ({
topology : "mesh" ,
maxAgents : 5 ,
strategy : "balanced"
})
mcp__flow-nexus__agent_spawn ({ type : "researcher" , name : "Primary Researcher" })
mcp__flow-nexus__agent_spawn ({ type : "researcher" , name : "Secondary Researcher" })
mcp__flow-nexus__agent_spawn ({ type : "analyst" , name : "Data Analyst" })
mcp__flow-nexus__agent_spawn ({ type : "analyst" , name : "Insights Analyst" })
mcp__flow-nexus__task_orchestrate ({
task : "Research machine learning trends for 2025 and analyze market opportunities" ,
strategy : "parallel" ,
maxAgents : 4 ,
priority : "high"
})
CI/CD Pipeline Pattern mcp__flow-nexus__workflow_create ({
name : "Deployment Pipeline" ,
description : "Automated testing, building, and multi-environment deployment" ,
steps : [
{ id : "lint" , action : "lint_code" , agent : "code_quality" , parallel : true },
{ id : "unit_test" , action : "unit_tests" , agent : "test_runner" , parallel : true },
{ id : "integration_test" , action : "integration_tests" , agent : "test_runner" , parallel : true },
{ id : "build" , action : "build_artifacts" , agent : "builder" , depends_on : ["lint" , "unit_test" , "integration_test" ] },
{ id : "security_scan" , action : "security_scan" , agent : "security" , depends_on : ["build" ] },
{ id : "deploy_staging" , action : "deploy" , agent : "deployer" , depends_on : ["security_scan" ] },
{ id : "smoke_test" , action : "smoke_tests" , agent : "test_runner" , depends_on : ["deploy_staging" ] },
{ id : "deploy_prod" , action : "deploy" , agent : "deployer" , depends_on : ["smoke_test" ] }
],
triggers : ["github_push" , "github_pr_merged" ],
metadata : {
priority : 10 ,
auto_rollback : true
}
})
Data Processing Pipeline Pattern mcp__flow-nexus__workflow_create ({
name : "ETL Pipeline" ,
description : "Extract, Transform, Load data processing" ,
steps : [
{ id : "extract" , action : "extract_data" , agent : "data_extractor" },
{ id : "validate_raw" , action : "validate_data" , agent : "validator" , depends_on : ["extract" ] },
{ id : "transform" , action : "transform_data" , agent : "transformer" , depends_on : ["validate_raw" ] },
{ id : "enrich" , action : "enrich_data" , agent : "enricher" , depends_on : ["transform" ] },
{ id : "load" , action : "load_data" , agent : "loader" , depends_on : ["enrich" ] },
{ id : "validate_final" , action : "validate_data" , agent : "validator" , depends_on : ["load" ] }
],
triggers : ["schedule:0 2 * * *" ],
metadata : {
retry_policy : "exponential_backoff" ,
max_retries : 3
}
})
Templates & Patterns
Use Pre-built Templates
mcp__flow-nexus__swarm_create_from_template ({
template_name : "full-stack-dev" ,
overrides : {
maxAgents : 6 ,
strategy : "specialized"
}
})
mcp__flow-nexus__swarm_templates_list ({
category : "quickstart" ,
includeStore : true
})
Available Template Categories:
full-stack-dev: Complete web development swarmresearch-team: Research and analysis swarmcode-review: Automated code review swarmdata-pipeline: ETL and data processing
ml-development: Machine learning project swarmmobile-dev: Mobile app developmentdevops-automation: Infrastructure and deploymentsecurity-audit: Security analysis and testing
enterprise-migration: Large-scale system migrationmulti-repo-sync: Multi-repository coordinationcompliance-review: Regulatory compliance workflowsincident-response: Automated incident management
Custom Template Creation Save successful swarm configurations as reusable templates for future projects.
Advanced Features
Real-time Monitoring
mcp__flow-nexus__execution_stream_subscribe ({
stream_type : "claude-flow-swarm" ,
deployment_id : "deployment_id"
})
mcp__flow-nexus__execution_stream_status ({
stream_id : "stream_id"
})
mcp__flow-nexus__execution_files_list ({
stream_id : "stream_id" ,
created_by : "claude-flow"
})
Swarm Metrics & Analytics
mcp__flow-nexus__swarm_status ({
swarm_id : "id"
})
mcp__flow-nexus__workflow_status ({
workflow_id : "id" ,
include_metrics : true
})
Multi-Swarm Coordination Coordinate multiple swarms for complex, multi-phase projects:
const researchSwarm = await mcp__flow-nexus__swarm_init ({
topology : "mesh" ,
maxAgents : 4
})
const devSwarm = await mcp__flow-nexus__swarm_init ({
topology : "hierarchical" ,
maxAgents : 8
})
const testSwarm = await mcp__flow-nexus__swarm_init ({
topology : "star" ,
maxAgents : 5
})
Best Practices
1. Choose the Right Topology
mcp__flow-nexus__swarm_init ({ topology : "star" , maxAgents : 3 })
mcp__flow-nexus__swarm_init ({ topology : "mesh" , maxAgents : 5 })
mcp__flow-nexus__swarm_init ({ topology : "hierarchical" , maxAgents : 10 })
mcp__flow-nexus__swarm_init ({ topology : "ring" , maxAgents : 4 })
2. Optimize Agent Assignment
mcp__flow-nexus__workflow_agent_assign ({
task_id : "complex-task" ,
use_vector_similarity : true
})
3. Implement Proper Error Handling mcp__flow-nexus__workflow_create ({
name : "Resilient Workflow" ,
steps : [...],
metadata : {
retry_policy : "exponential_backoff" ,
max_retries : 3 ,
timeout : 300000 ,
on_failure : "notify_and_rollback"
}
})
4. Monitor and Scale
const status = await mcp__flow-nexus__swarm_status ()
if (status.workload > 0.8 ) {
await mcp__flow-nexus__swarm_scale ({ target_agents : status.agents + 2 })
}
5. Use Async Execution for Long-Running Workflows
mcp__flow-nexus__workflow_execute ({
workflow_id : "data-pipeline" ,
async : true
})
mcp__flow-nexus__workflow_queue_status ({ include_messages : true })
6. Clean Up Resources
mcp__flow-nexus__swarm_destroy ({ swarm_id : "id" })
7. Leverage Templates
mcp__flow-nexus__swarm_create_from_template ({
template_name : "code-review" ,
overrides : { maxAgents : 4 }
})
Integration with Claude Flow Flow Nexus swarms integrate seamlessly with Claude Flow hooks:
npx claude-flow@alpha hooks pre-task --description "Initialize swarm"
npx claude-flow@alpha hooks post-task --task-id "swarm-execution"
Common Use Cases
1. Multi-Repo Development
Coordinate development across multiple repositories
Synchronized testing and deployment
Cross-repo dependency management
2. Research Projects
Distributed information gathering
Parallel analysis of different data sources
Collaborative synthesis and reporting
3. DevOps Automation
Infrastructure as Code deployment
Multi-environment testing
Automated rollback and recovery
4. Code Quality Workflows
Automated code review
Security scanning
Performance benchmarking
5. Data Processing
Large-scale ETL pipelines
Real-time data transformation
Data validation and quality checks
Authentication & Setup
npm install -g flow-nexus@latest
npx flow-nexus@latest register
npx flow-nexus@latest login
claude mcp add flow-nexus npx flow-nexus@latest mcp start
Support & Resources
Remember : Flow Nexus provides cloud-based orchestration infrastructure. For local execution and coordination, use the core claude-flow MCP server alongside Flow Nexus for maximum flexibility.
